CARRERA:
Computación

ESTUDIANTE:
Christian Mocha

PRÁCTICA DE LABORATORIO ASIGNATURA:
Simulación

ACTIVIDADES DESARROLLADAS

Carga de los datos de los jegadores de FIFA

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import plotly.express as px 
import numpy 

df = pd.read_csv('fifa_datos.csv')
df

	Columna	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
0	0	158023	L. Messi	31	https://cdn.sofifa.org/players/4/19/158023.png	Argentina	https://cdn.sofifa.org/flags/52.png	94	94	FC Barcelona	...	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0	€226.5M
1	1	20801	Cristiano Ronaldo	33	https://cdn.sofifa.org/players/4/19/20801.png	Portugal	https://cdn.sofifa.org/flags/38.png	94	94	Juventus	...	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0	€127.1M
2	2	190871	Neymar Jr	26	https://cdn.sofifa.org/players/4/19/190871.png	Brazil	https://cdn.sofifa.org/flags/54.png	92	93	Paris Saint-Germain	...	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0	€228.1M
3	3	193080	De Gea	27	https://cdn.sofifa.org/players/4/19/193080.png	Spain	https://cdn.sofifa.org/flags/45.png	91	93	Manchester United	...	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0	€138.6M
4	4	192985	K. De Bruyne	27	https://cdn.sofifa.org/players/4/19/192985.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	92	Manchester City	...	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0	€196.4M
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18202	18202	238813	J. Lundstram	19	https://cdn.sofifa.org/players/4/19/238813.png	England	https://cdn.sofifa.org/flags/14.png	47	65	Crewe Alexandra	...	45.0	40.0	48.0	47.0	10.0	13.0	7.0	8.0	9.0	€143K
18203	18203	243165	N. Christoffersson	19	https://cdn.sofifa.org/players/4/19/243165.png	Sweden	https://cdn.sofifa.org/flags/46.png	47	63	Trelleborgs FF	...	42.0	22.0	15.0	19.0	10.0	9.0	9.0	5.0	12.0	€113K
18204	18204	241638	B. Worman	16	https://cdn.sofifa.org/players/4/19/241638.png	England	https://cdn.sofifa.org/flags/14.png	47	67	Cambridge United	...	41.0	32.0	13.0	11.0	6.0	5.0	10.0	6.0	13.0	€165K
18205	18205	246268	D. Walker-Rice	17	https://cdn.sofifa.org/players/4/19/246268.png	England	https://cdn.sofifa.org/flags/14.png	47	66	Tranmere Rovers	...	46.0	20.0	25.0	27.0	14.0	6.0	14.0	8.0	9.0	€143K
18206	18206	246269	G. Nugent	16	https://cdn.sofifa.org/players/4/19/246269.png	England	https://cdn.sofifa.org/flags/14.png	46	66	Tranmere Rovers	...	43.0	40.0	43.0	50.0	10.0	15.0	9.0	12.0	9.0	€165K

18207 rows × 89 columns

p1 = 0
p2 = 0
p3 = 0
p4 = 0
separador = "lbs"
for x in df['Weight']:
    separado = str(x).split(separador)
    x1 = float(separado[0])
    if x1 < 125:
        p1+=1
    elif x1 < 150:
        p2+=1
    elif x1 < 175:
        p3+=1
    else:
        p4+=1
pesos = [p1, p2, p3, p4]
pesos

[41, 2290, 10876, 5000]

Con los datos de los jugadores de FIFA, organizarlos de acuerdo al peso (debajo de las 125 lbs, 125 - 150 lbs, 150 - 175 lbs, 175 lbs en adelante) por medio de un gráfico de pie.

random_x = [41, 2290, 10876, 5000] 
names = ['<125 lbs', '125 - 150 lbs', '150 - 175 lbs', '>175 lbs'] 
  
fig = px.pie(values=random_x, names=names) 
fig.show()

Generar un gráfico de barras de acuerdo al overall de los jugadores en base a los siguientes segmentos:

• 40
• 50
• 60
• 70
• 80
• 90
• 100

x1; x2=0; x3=0; x4=0; x5=0; x6 = 0
for x in df['Overall']:
    if x < 50:
        x1+=1
    elif x < 60:
        x2 +=1
    elif x < 70:
        x3+=1
    elif x < 80:
        x4+=1
    elif x < 90:
        x5+=1
    else:
        x6+=1
overall = [x1, x2, x3, x4, x5, x6]
print(overall)

[265.0, 2815, 9665, 5083, 541, 14]

plt.figure(figsize=(10,12))
plt.bar(['40-50', '50-60', '60-70', '70-80', '80-90', '90-100'], overall, color='lightsteelblue', width=0.9)
plt.yticks(np.arange(0, 10001, 500))
plt.grid(linestyle='--', linewidth=0.5)
plt.show()

Investigar alguna herramienta o software que permita generar reportes con Python

• ReportLab